Diagnostic Methods in Time Series

Beschreibung

This book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of tests. This is the first book to cover systematic approaches and widely applicable results for nonstandard models including infinite variance processes. The book begins by introducing a unified view of a portmanteau-type test based on a likelihood ratio test, useful to test general parametric hypotheses inherent in statistical models. The conditions for the limit distribution of portmanteau-type tests to be asymptotically pivotal are given under general settings, and very clear implications for the relationships between the parameter of interest and the nuisance parameter are elucidated in terms of Fisher-information matrices. A robust testing procedure against heavy-tailed time series models is also constructed in the context of variable selection problems. The setting is very reasonable in the context of financial data analysis and econometrics, and the result is applicable to causality tests of heavy-tailed time series models. In the last two sections, Bartlett-type adjustments for a class of test statistics are discussed when the parameter of interest is on the boundary of the parameter space. A nonlinear adjustment procedure is proposed for a broad range of test statistics including the likelihood ratio, Wald and score statistics.
Covers a broad range of techniques for model diagnostics of time series models under general settings Provides robust testing procedures including variable selection and causality without any moment conditions Develops higher-order asymptotics of tests when the parameter of interest is on the parameter space boundary

Autor*in

Fumiya Akashi

Themen in »Diagnostic Methods in Time Series«

Time Series Analysis Infinite Variance Process Variable Selection Test of Causality Empirical Likelihood Higher-order Asymptotic Theory Bartlett Adjustment Whittle Likelihood Analysis of Variance

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